


"""simple docstring"""

import argparse

import torch

# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./  # create simlink
# import classes
from unilm.wavlm.WavLM import WavLM as WavLMOrig
from unilm.wavlm.WavLM import WavLMConfig as WavLMConfigOrig

from transformers import WavLMConfig, WavLMModel, logging


logging.set_verbosity_info()
UpperCAmelCase__					=logging.get_logger(__name__)

UpperCAmelCase__					={
    'post_extract_proj': 'feature_projection.projection',
    'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
    'self_attn.k_proj': 'encoder.layers.*.attention.k_proj',
    'self_attn.v_proj': 'encoder.layers.*.attention.v_proj',
    'self_attn.q_proj': 'encoder.layers.*.attention.q_proj',
    'self_attn.out_proj': 'encoder.layers.*.attention.out_proj',
    'self_attn.grep_linear': 'encoder.layers.*.attention.gru_rel_pos_linear',
    'self_attn.relative_attention_bias': 'encoder.layers.*.attention.rel_attn_embed',
    'self_attn.grep_a': 'encoder.layers.*.attention.gru_rel_pos_const',
    'self_attn_layer_norm': 'encoder.layers.*.layer_norm',
    'fc1': 'encoder.layers.*.feed_forward.intermediate_dense',
    'fc2': 'encoder.layers.*.feed_forward.output_dense',
    'final_layer_norm': 'encoder.layers.*.final_layer_norm',
    'encoder.layer_norm': 'encoder.layer_norm',
    'w2v_model.layer_norm': 'feature_projection.layer_norm',
    'quantizer.weight_proj': 'quantizer.weight_proj',
    'quantizer.vars': 'quantizer.codevectors',
    'project_q': 'project_q',
    'final_proj': 'project_hid',
    'w2v_encoder.proj': 'ctc_proj',
    'mask_emb': 'masked_spec_embed',
}
UpperCAmelCase__					=[
    'ctc_proj',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]


def lowerCAmelCase_   (					UpperCamelCase__						:			Any					, UpperCamelCase__						:			Optional[int]					, UpperCamelCase__						:			Any					, UpperCamelCase__						:			List[str]					, UpperCamelCase__						:			List[Any]			):






 """simple docstring"""







 for attribute in key.split("""."""			):
  __lowercase						=						getattr(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__			)

 if weight_type is not None:
  __lowercase						=						getattr(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__			).shape
 else:
  __lowercase						=						hf_pointer.shape

 assert hf_shape == value.shape, (
     f'''Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be'''
     f''' {value.shape} for {full_name}'''
 )

 if weight_type == "weight":
  __lowercase						=						value
 elif weight_type == "weight_g":
  __lowercase						=						value
 elif weight_type == "weight_v":
  __lowercase						=						value
 elif weight_type == "bias":
  __lowercase						=						value
 else:
  __lowercase						=						value

 logger.info(f'''{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.'''			)


def lowerCAmelCase_   (					UpperCamelCase__						:			int					, UpperCamelCase__						:			Optional[int]			):






 """simple docstring"""







 __lowercase						=						[]
 __lowercase						=						fairseq_model.state_dict()

 __lowercase						=						hf_model.feature_extractor

 for name, value in fairseq_dict.items():
  __lowercase						=						False
  if "conv_layers" in name:
   load_conv_layer(
       SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, hf_model.config.feat_extract_norm == """group"""					, )
   __lowercase						=						True
  else:
   for key, mapped_key in MAPPING.items():
    if key in name or key.split("""w2v_model."""			)[-1] == name.split("""."""			)[0]:
     __lowercase						=						True
     if "*" in mapped_key:
      __lowercase						=						name.split(SCREAMING_SNAKE_CASE__			)[0].split("""."""			)[-2]
      __lowercase						=						mapped_key.replace("""*"""					, SCREAMING_SNAKE_CASE__			)
     if "weight_g" in name:
      __lowercase						=						"""weight_g"""
     elif "weight_v" in name:
      __lowercase						=						"""weight_v"""
     elif "bias" in name and "relative_attention_bias" not in name:
      __lowercase						=						"""bias"""
     elif "weight" in name:
      # TODO: don't match quantizer.weight_proj
      __lowercase						=						"""weight"""
     else:
      __lowercase						=						None

     set_recursively(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__			)
    continue
  if not is_used:
   unused_weights.append(SCREAMING_SNAKE_CASE__			)

 logger.warning(f'''Unused weights: {unused_weights}'''			)


def lowerCAmelCase_   (					UpperCamelCase__						:			Any					, UpperCamelCase__						:			List[Any]					, UpperCamelCase__						:			Optional[int]					, UpperCamelCase__						:			List[Any]					, UpperCamelCase__						:			Union[str, Any]			):






 """simple docstring"""







 __lowercase						=						full_name.split("""conv_layers."""			)[-1]
 __lowercase						=						name.split("""."""			)
 __lowercase						=						int(items[0]			)
 __lowercase						=						int(items[1]			)

 if type_id == 0:
  if "bias" in name:
   assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
       f'''{full_name} has size {value.shape}, but'''
       f''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''
   )
   __lowercase						=						value
   logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''			)
  elif "weight" in name:
   assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
       f'''{full_name} has size {value.shape}, but'''
       f''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''
   )
   __lowercase						=						value
   logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''			)
 elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
  if "bias" in name:
   assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
       f'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
       " found."
   )
   __lowercase						=						value
   logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''			)
  elif "weight" in name:
   assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
       f'''{full_name} has size {value.shape}, but'''
       f''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''
   )
   __lowercase						=						value
   logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''			)
 else:
  unused_weights.append(SCREAMING_SNAKE_CASE__			)


@torch.no_grad()
def lowerCAmelCase_   (					UpperCamelCase__						:			Dict					, UpperCamelCase__						:			int					, UpperCamelCase__						:			List[str]=None			):






 """simple docstring"""







 __lowercase						=						torch.load(SCREAMING_SNAKE_CASE__			)
 __lowercase						=						WavLMConfigOrig(checkpoint["""cfg"""]			)
 __lowercase						=						WavLMOrig(SCREAMING_SNAKE_CASE__			)
 model.load_state_dict(checkpoint["""model"""]			)
 model.eval()

 if config_path is not None:
  __lowercase						=						WavLMConfig.from_pretrained(SCREAMING_SNAKE_CASE__			)
 else:
  __lowercase						=						WavLMConfig()

 __lowercase						=						WavLMModel(SCREAMING_SNAKE_CASE__			)

 recursively_load_weights(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__			)

 hf_wavlm.save_pretrained(SCREAMING_SNAKE_CASE__			)


if __name__ == "__main__":
       UpperCAmelCase__					=argparse.ArgumentParser()
       parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
       parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
       parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
       UpperCAmelCase__					=parser.parse_args()
       convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)




'''simple docstring'''


import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
				from PIL import Image

				from transformers import CLIPSegProcessor, ViTImageProcessor





@require_vision
class        lowercase__    (		unittest.TestCase   ):





							'''simple docstring'''


							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			List[str]							= tempfile.mkdtemp()

											# fmt: off
											_SCREAMING_SNAKE_CASE					:			Any							= ["""l""", """o""", """w""", """e""", """r""", """s""", """t""", """i""", """d""", """n""", """lo""", """l</w>""", """w</w>""", """r</w>""", """t</w>""", """low</w>""", """er</w>""", """lowest</w>""", """newer</w>""", """wider""", """<unk>""", """<|startoftext|>""", """<|endoftext|>"""]
											# fmt: on
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= dict(zip(__snake_case       ,   range(len(__snake_case    )    )    )    )
											_SCREAMING_SNAKE_CASE					:			str							= ["""#version: 0.2""", """l o""", """lo w</w>""", """e r</w>""", """"""]
											_SCREAMING_SNAKE_CASE					:			Any							= {"""unk_token""": """<unk>"""}

											_SCREAMING_SNAKE_CASE					:			Optional[int]							= os.path.join(self.tmpdirname       ,   VOCAB_FILES_NAMES["""vocab_file"""]    )
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= os.path.join(self.tmpdirname       ,   VOCAB_FILES_NAMES["""merges_file"""]    )
											with open(self.vocab_file       ,   """w"""       ,   encoding="""utf-8"""    ) as fp:
															fp.write(json.dumps(__snake_case    ) + """\n"""    )
											with open(self.merges_file       ,   """w"""       ,   encoding="""utf-8"""    ) as fp:
															fp.write("""\n""".join(__snake_case    )    )

											_SCREAMING_SNAKE_CASE					:			Any							= {
											    """do_resize""": True,
											    """size""": 20,
											    """do_center_crop""": True,
											    """crop_size""": 18,
											    """do_normalize""": True,
											    """image_mean""": [0.48145466, 0.4578275, 0.40821073],
											    """image_std""": [0.26862954, 0.26130258, 0.27577711],
											}
											_SCREAMING_SNAKE_CASE					:			List[str]							= os.path.join(self.tmpdirname       ,   __snake_case    )
											with open(self.image_processor_file       ,   """w"""       ,   encoding="""utf-8"""    ) as fp:
															json.dump(__snake_case       ,   __snake_case    )






							def UpperCAmelCase_      (       self       ,   **__snake_case    ):
											return CLIPTokenizer.from_pretrained(self.tmpdirname       ,   **__snake_case    )






							def UpperCAmelCase_      (       self       ,   **__snake_case    ):
											return CLIPTokenizerFast.from_pretrained(self.tmpdirname       ,   **__snake_case    )






							def UpperCAmelCase_      (       self       ,   **__snake_case    ):
											return ViTImageProcessor.from_pretrained(self.tmpdirname       ,   **__snake_case    )






							def UpperCAmelCase_      (       self    ):
											shutil.rmtree(self.tmpdirname    )






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= [np.random.randint(255       ,   size=(3, 30, 400)       ,   dtype=np.uinta    )]

											_SCREAMING_SNAKE_CASE					:			List[str]							= [Image.fromarray(np.moveaxis(__snake_case       ,   0       ,   -1    )    ) for x in image_inputs]

											return image_inputs






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			List[str]							= self.get_tokenizer()
											_SCREAMING_SNAKE_CASE					:			int							= self.get_rust_tokenizer()
											_SCREAMING_SNAKE_CASE					:			Dict							= self.get_image_processor()

											_SCREAMING_SNAKE_CASE					:			str							= CLIPSegProcessor(tokenizer=__snake_case       ,   image_processor=__snake_case    )
											processor_slow.save_pretrained(self.tmpdirname    )
											_SCREAMING_SNAKE_CASE					:			List[str]							= CLIPSegProcessor.from_pretrained(self.tmpdirname       ,   use_fast=__snake_case    )

											_SCREAMING_SNAKE_CASE					:			Any							= CLIPSegProcessor(tokenizer=__snake_case       ,   image_processor=__snake_case    )
											processor_fast.save_pretrained(self.tmpdirname    )
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= CLIPSegProcessor.from_pretrained(self.tmpdirname    )

											self.assertEqual(processor_slow.tokenizer.get_vocab()       ,   tokenizer_slow.get_vocab()    )
											self.assertEqual(processor_fast.tokenizer.get_vocab()       ,   tokenizer_fast.get_vocab()    )
											self.assertEqual(tokenizer_slow.get_vocab()       ,   tokenizer_fast.get_vocab()    )
											self.assertIsInstance(processor_slow.tokenizer       ,   __snake_case    )
											self.assertIsInstance(processor_fast.tokenizer       ,   __snake_case    )

											self.assertEqual(processor_slow.image_processor.to_json_string()       ,   image_processor.to_json_string()    )
											self.assertEqual(processor_fast.image_processor.to_json_string()       ,   image_processor.to_json_string()    )
											self.assertIsInstance(processor_slow.image_processor       ,   __snake_case    )
											self.assertIsInstance(processor_fast.image_processor       ,   __snake_case    )






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			int							= CLIPSegProcessor(tokenizer=self.get_tokenizer()       ,   image_processor=self.get_image_processor()    )
											processor.save_pretrained(self.tmpdirname    )

											_SCREAMING_SNAKE_CASE					:			str							= self.get_tokenizer(bos_token="""(BOS)"""       ,   eos_token="""(EOS)"""    )
											_SCREAMING_SNAKE_CASE					:			List[str]							= self.get_image_processor(do_normalize=__snake_case       ,   padding_value=1.0    )

											_SCREAMING_SNAKE_CASE					:			Tuple							= CLIPSegProcessor.from_pretrained(
											    self.tmpdirname       ,   bos_token="""(BOS)"""       ,   eos_token="""(EOS)"""       ,   do_normalize=__snake_case       ,   padding_value=1.0    )

											self.assertEqual(processor.tokenizer.get_vocab()       ,   tokenizer_add_kwargs.get_vocab()    )
											self.assertIsInstance(processor.tokenizer       ,   __snake_case    )

											self.assertEqual(processor.image_processor.to_json_string()       ,   image_processor_add_kwargs.to_json_string()    )
											self.assertIsInstance(processor.image_processor       ,   __snake_case    )






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= self.get_image_processor()
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= self.get_tokenizer()

											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= CLIPSegProcessor(tokenizer=__snake_case       ,   image_processor=__snake_case    )

											_SCREAMING_SNAKE_CASE					:			List[Any]							= self.prepare_image_inputs()

											_SCREAMING_SNAKE_CASE					:			List[Any]							= image_processor(__snake_case       ,   return_tensors="""np"""    )
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= processor(images=__snake_case       ,   return_tensors="""np"""    )

											for key in input_feat_extract.keys():
															self.assertAlmostEqual(input_feat_extract[key].sum()       ,   input_processor[key].sum()       ,   delta=1e-2    )






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			List[str]							= self.get_image_processor()
											_SCREAMING_SNAKE_CASE					:			Dict							= self.get_tokenizer()

											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= CLIPSegProcessor(tokenizer=__snake_case       ,   image_processor=__snake_case    )

											_SCREAMING_SNAKE_CASE					:			str							= """lower newer"""

											_SCREAMING_SNAKE_CASE					:			Any							= processor(text=__snake_case    )

											_SCREAMING_SNAKE_CASE					:			Tuple							= tokenizer(__snake_case    )

											for key in encoded_tok.keys():
															self.assertListEqual(encoded_tok[key]       ,   encoded_processor[key]    )






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= self.get_image_processor()
											_SCREAMING_SNAKE_CASE					:			List[Any]							= self.get_tokenizer()

											_SCREAMING_SNAKE_CASE					:			Dict							= CLIPSegProcessor(tokenizer=__snake_case       ,   image_processor=__snake_case    )

											_SCREAMING_SNAKE_CASE					:			Any							= """lower newer"""
											_SCREAMING_SNAKE_CASE					:			List[Any]							= self.prepare_image_inputs()

											_SCREAMING_SNAKE_CASE					:			List[str]							= processor(text=__snake_case       ,   images=__snake_case    )

											self.assertListEqual(list(inputs.keys()    )       ,   ["""input_ids""", """attention_mask""", """pixel_values"""]    )

											# test if it raises when no input is passed
											with pytest.raises(__snake_case    ):
															processor()






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			Dict							= self.get_image_processor()
											_SCREAMING_SNAKE_CASE					:			Tuple							= self.get_tokenizer()

											_SCREAMING_SNAKE_CASE					:			List[Any]							= CLIPSegProcessor(tokenizer=__snake_case       ,   image_processor=__snake_case    )

											_SCREAMING_SNAKE_CASE					:			str							= self.prepare_image_inputs()
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= self.prepare_image_inputs()

											_SCREAMING_SNAKE_CASE					:			List[Any]							= processor(images=__snake_case       ,   visual_prompt=__snake_case    )

											self.assertListEqual(list(inputs.keys()    )       ,   ["""pixel_values""", """conditional_pixel_values"""]    )

											# test if it raises when no input is passed
											with pytest.raises(__snake_case    ):
															processor()






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= self.get_image_processor()
											_SCREAMING_SNAKE_CASE					:			Dict							= self.get_tokenizer()

											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= CLIPSegProcessor(tokenizer=__snake_case       ,   image_processor=__snake_case    )

											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

											_SCREAMING_SNAKE_CASE					:			int							= processor.batch_decode(__snake_case    )
											_SCREAMING_SNAKE_CASE					:			int							= tokenizer.batch_decode(__snake_case    )

											self.assertListEqual(__snake_case       ,   __snake_case    )




